An intelligent system and method for comprehensive nursing management and evaluation after gynecological tumor surgery

Abstract

The present invention discloses an intelligent system and method for comprehensive nursing management and evaluation after gynecological tumor surgery. The method comprises the following steps: S1, collecting physiological data and subjective data for preprocessing; S2, establishing a mathematical model for evaluating pain level based on physiological data and subjective data; S3, integrating the mathematical model into the evaluation system, receiving early postoperative physiological data, and giving adjustment suggestions for ice compress intensity according to the pain level; S4, receiving late postoperative physiological data, and giving matching suggestions for drug dosage according to the pain level. The closed-loop nursing management and evaluation process of data management, data processing, mathematical model and evaluation system performs comprehensive analysis based on the real-time feedback of the patient's pain, automatically identifies the pain pattern, improves the accuracy and real-time performance of the evaluation, adjusts the ice compress intensity or drug dosage, and dynamically adjusts the treatment plan to ensure the flexibility and effectiveness of the treatment plan, thereby achieving a more comprehensive pain evaluation.

Description

Technical Field

[0001] The present invention relates to the field of clinical nursing technology, in particular to guided treatment, and specifically to an intelligent system and method for comprehensive nursing management and evaluation after gynecological tumor surgery. Background Art

[0002] In the field of gynecological tumor treatment, tumor microwave therapy, as an advanced non-invasive or minimally invasive treatment method, has received widespread attention and application in recent years. Tumor microwave therapy, especially microwave ablation therapy, is a method that uses the thermal effect of microwaves to achieve the purpose of treatment. In the field of gynecology, it has been used for uterine fibroids, cervical fibroids, etc. Its principle can be briefly described as follows: the heat energy generated by microwaves causes the temperature inside the tumor tissue to rise rapidly, thereby causing the tumor cells to coagulate and necrotize, thereby achieving the effect of reducing or eliminating the tumor. Specifically, the microwave energy generated by the microwave ablation device can be directly introduced into the tumor tissue through a special needle radiator (microwave antenna). The microwave energy is converted into heat energy inside the tumor, causing the temperature of the tumor tissue to rise rapidly to 50-100°C, or even higher. This high-temperature environment can cause protein denaturation, cell membrane rupture, and damage to genetic materials such as DNA and RNA in the cell nucleus in tumor cells, thereby causing the tumor cells to lose their ability to proliferate and achieve the purpose of treatment. It has the advantages of minimally invasive, uterine preservation, fertility preservation, no damage to ovarian function, short hospitalization time, less pain, fast recovery, and no scars on the skin, and is highly accepted by patients.

[0003] After microwave therapy for tumors, due to the residual effect of microwave heat and tissue damage, patients often experience varying degrees of pain in the early postoperative period (such as within 1-2 days), which not only affects the patient's comfort, but may also affect their recovery process and mental health. As time passes after microwave therapy, inflammation or edema may occur around the tissue in the later period after surgery (such as 2 days), which not only increases the patient's pain, but may also prolong hospitalization and affect the treatment effect. Medical staff need to give appropriate analgesics or physical therapy, such as cold compresses, hot compresses, etc., according to the degree of pain to relieve the patient's pain. At present, the nursing management and evaluation after microwave therapy for gynecological tumors mainly rely on the experience and judgment of medical staff and traditional nursing methods, and lack a scientific and objective evaluation system and personalized treatment plans.

[0004] Therefore, it is necessary to improve the deficiencies in the prior art to solve the above problems. Summary of the invention

[0005] The present invention overcomes the deficiencies of the prior art and provides an intelligent system and method for comprehensive nursing management and evaluation after gynecological tumor surgery.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a comprehensive nursing management and evaluation method for gynecological tumor surgery, comprising the following steps:

[0007] S1. Collect physiological data and subjective data of different patients at different periods after surgery, and pre-process the collected physiological data;

[0008] S2. Build a mathematical model to assess pain severity based on physiological and subjective data;

[0009] S3, integrating the mathematical model into the evaluation system, receiving the patient's real-time physiological data in the early postoperative period, using the mathematical model to obtain the real-time pain level value, and according to the pain level value, the evaluation system calculates and gives the adjustment suggestion of the ice compress intensity;

[0010] S4. The evaluation system receives the patient's real-time physiological data in the late postoperative period, and uses a mathematical model to obtain a real-time pain level value. Based on the pain level value, the evaluation system calculates and gives matching recommendations for drug dosage.

[0011] In a preferred embodiment of the present invention, the step S1 includes the following sub-steps:

[0012] S11. Collect physiological data of different patients in the early and late stages after surgery. When collecting physiological data of patients, use a pain rating scale to collect subjective data of pain.

[0013] S12. From the collected physiological data, select features that are highly correlated with pain changes in subjective data;

[0014] S13. Clean, standardize and normalize the collected physiological data to ensure the consistency and comparability of the data.

[0015] In a preferred embodiment of the present invention, in step S11, the initial period is 1-2 days after surgery, and the late period is more than 2 days after surgery; the physiological data include: skin conductivity, muscle potential changes, heart rate variability, blood pressure, respiratory rate and skin temperature; the pain rating scale is one of VAS or NRS.

[0016] In a preferred embodiment of the present invention, in step S2, the establishment of the mathematical model includes the following steps:

[0017] S21, performing correlation analysis on the physiological data screened in step S12;

[0018] S22. Assign weights to physiological data that influence pain;

[0019] S23. Output a mathematical model for assessing pain severity.

[0020] In a preferred embodiment of the present invention, the physiological data is used as an influencing factor for establishing a mathematical model, and the Pearson correlation coefficient r is calculated for each influencing factor x and pain level y: Among them, x i is the physiological data value of the i-th sample, y i is with x i The corresponding pain level of the i-th sample, is the mean of the physiological data, is the mean pain level, n is the sample size;

[0021] Calculate the entropy value e of each influencing factor j : in, f ij is the value of the i-th sample on the j-th influencing factor, p ij Indicates the weight of the i-th sample on the j-th influencing factor;

[0022] The coefficient of difference d reflects the degree of dispersion of the indicator. j :d j =1-e j , combined with the correlation adjustment, the coefficient of difference is d j ′=d j ·|r j |, where r j is the correlation coefficient between the jth influencing factor x and the pain level y;

[0023] Use the adjusted coefficient of variation to calculate the weight w of each influencing factor j :

[0024]

[0025] After calculating the weights, the corresponding mathematical model is obtained: p is the predicted pain level, β 0 is the intercept term.

[0026] In a preferred embodiment of the present invention, step S3 includes the following sub-steps:

[0027] S31, encapsulate the mathematical model into an independent module, and define the interface between the mathematical model and the evaluation system, including the input data format, output data format, and calling method involved;

[0028] S32, integrating the packaged mathematical model module into the evaluation system, realizing the connection between the system and the data source, and ensuring that the system can obtain the physiological data to be evaluated in real time;

[0029] S33, inputting the patient's real-time physiological data in the early postoperative period into the evaluation system, using a mathematical model to obtain a real-time pain level value, and the evaluation system calculates and gives adjustment suggestions for the ice compress intensity based on the pain level value;

[0030] Ice intensity g p : Use piecewise function to output the numerical code according to the predicted pain level p, Among them, a, b, and c are the thresholds of different pain levels in the early postoperative period, and 1, 2, 3, and 4 are the output numerical codes of the ice compress intensity.

[0031] In a preferred embodiment of the present invention, in step S4, the drug amount k p : Use piecewise function to output the numerical code according to the predicted pain level p,

[0032] d, e, and f are the thresholds for different pain levels in the late postoperative period, and 1, 2, 3, and 4 are the numerical codes for the output drug amounts.

[0033] In a preferred embodiment of the present invention, the thresholds for different pain severity ranges are set based on clinical experience, experimental data or expert experience.

[0034] The present invention provides an intelligent system for comprehensive nursing management and evaluation method after gynecological tumor surgery, comprising:

[0035] The data management module is used to input the patient's physiological data and store the patient's basic information for classification management;

[0036] Data processing module, used to pre-process the collected physiological data to ensure the consistency and comparability of the data;

[0037] a mathematical model for outputting predicted pain assessment data based on the physiological data;

[0038] The evaluation system is used to output adjustment suggestions for ice compress intensity and matching suggestions for drug dosage based on pain assessment data in the early and late stages after surgery respectively.

[0039] In a preferred embodiment of the present invention, the patient's physiological data is input into the data management module, and the patient's basic information is stored for classification management. The data management module inputs the collected physiological data into the data processing module to preprocess the collected physiological data. The data processing module inputs the preprocessed physiological data into the mathematical model to predict pain assessment data. The assessment system outputs adjustment suggestions for ice compress intensity in the early postoperative period based on the pain assessment data, and outputs matching suggestions for drug dosage in the later postoperative period.

[0040] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0041] (1) The present invention provides an intelligent system and method for comprehensive nursing management and evaluation after gynecological tumor surgery. The system captures the patient's neural activity signals, collects the patient's physiological data and subjective data closely related to the pain response, and constructs a closed-loop nursing management and evaluation process of data management, data processing, mathematical model and evaluation system based on the physiological data and subjective data. The system can comprehensively analyze the collected multi-source data based on the patient's real-time feedback on pain, automatically identify pain patterns, improve the accuracy and real-time nature of the evaluation, adjust the ice compress intensity or drug dosage, and dynamically adjust the treatment plan to ensure the flexibility and effectiveness of the treatment plan, thereby achieving a more comprehensive pain evaluation.

[0042] (2) In the present invention, by respectively targeting the differences in changes in the patient's pain perception in the early and late postoperative periods, and by accurately evaluating and predicting the pain level, the pain management plan can be personalized and recommended based on the predicted pain level. This fully considers the individual differences and pain characteristics of the patients, realizes personalized treatment, and ensures that ice therapy or drug adjustment is always matched with the patient's pain level to achieve the best analgesic effect, while avoiding adverse reactions caused by excessive cold stimulation. By combining accurate evaluation with personalized treatment, the patient's pain and discomfort can be significantly reduced, thereby improving the overall quality of postoperative recovery.

[0043] (3) In the present invention, the intelligent system can automatically complete the collection, processing and analysis of patients' physiological data, which greatly reduces the workload of medical staff. Medical staff only need to focus on the evaluation results and the formulation and implementation of treatment plans, without spending a lot of time on tedious data processing. Based on a large number of patients' historical data and real-time monitoring results, the use of data analysis and mathematical algorithms can provide scientific decision-making support for medical staff, including pain management strategies, ice compress intensity adjustment recommendations, and drug treatment plans, which helps medical staff make more reasonable and effective nursing decisions and ensure that patients get the best treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a flow chart of a method for comprehensive nursing management and evaluation after gynecological tumor surgery in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0048] like Figure 1 As shown, a comprehensive nursing management and evaluation method for gynecological tumor surgery includes the following steps:

[0049] S1. Collect physiological data and subjective data of different patients at different periods after surgery, and pre-process the collected physiological data;

[0050] S2. Based on physiological data and subjective data, a mathematical model for assessing pain severity is established, and the mathematical model is verified using measured physiological data to ensure the accuracy of the pain severity assessment;

[0051] S3, integrating the mathematical model into the evaluation system, receiving the patient's real-time physiological data in the early postoperative period, using the mathematical model to obtain the real-time pain level value, and according to the pain level value, the evaluation system calculates and gives the adjustment suggestion of the ice compress intensity;

[0052] S4. The evaluation system receives the patient's real-time physiological data in the late postoperative period, and uses a mathematical model to obtain a real-time pain level value. Based on the pain level value, the evaluation system calculates and gives matching recommendations for drug dosage.

[0053] It should be noted that by capturing the patient's neural activity signals (heart rate variability, skin electrical response, etc.), collecting the patient's physiological data and subjective data closely related to the pain response, and building a closed-loop nursing management and evaluation process of data management, data processing, mathematical model and evaluation system based on the physiological data and subjective data, it can conduct a comprehensive analysis of the collected multi-source data based on the patient's real-time feedback on pain, automatically identify pain patterns, improve the accuracy and real-time nature of the evaluation, adjust the intensity of ice compresses or the amount of medication, and dynamically adjust the treatment plan to ensure the flexibility and effectiveness of the treatment plan, thereby achieving a more comprehensive pain assessment.

[0054] In some specific embodiments, in step S1, the following sub-steps are included:

[0055] S11. Collect physiological data of different patients in the early and late stages after surgery. When collecting physiological data of patients, use a pain rating scale to collect subjective data of pain.

[0056] S12. From the collected physiological data, select features that are highly correlated with pain changes in subjective data;

[0057] S13. Clean, standardize and normalize the collected physiological data to ensure the consistency and comparability of the data.

[0058] In some specific embodiments, in step S11, the initial period is 1-2 days after surgery, and the later period is more than 2 days after surgery; the physiological data include: skin conductivity, muscle potential changes, heart rate variability, blood pressure, respiratory rate and skin temperature; the pain rating scale is one of VAS (visual analog scale) or NRS (numeric rating scale).

[0059] It should be noted that physiological data are obtained by collecting the patient's neural activity and physiological responses through electrode sensors, multi-parameter monitors, etc., and subjective data are obtained by quantitatively scoring the pain based on the patient's feelings through VAS or NRS.

[0060] In some specific implementation schemes, in step S13, the collected physiological data needs to be cleaned to remove duplicate data, identify and process abnormal values: abnormal values ​​may be caused by data entry errors or special circumstances of patients, and need to be processed according to actual conditions, such as replacing with mean, median or deletion, and processing missing values ​​at the same time: they can be processed by filling (such as filling with mean, median, mode), interpolation or deletion of records containing missing values. Since physiological data have different dimensions and ranges, physiological data of different dimensions are converted to the same scale, and the physiological data are normalized. The data are scaled proportionally to fall into a small specific interval, such as [0,1] or [-1,1], which helps to eliminate the impact of dimensions on the results. Minimum or maximum normalization, Z-score normalization, etc. can be used to facilitate subsequent analysis.

[0061] In some specific embodiments, in step S2, the establishment of the mathematical model includes the following steps:

[0062] S21, performing correlation analysis on the physiological data screened in step S12;

[0063] S22. Assign weights to physiological data that influence pain;

[0064] S23. Output a mathematical model for assessing pain severity.

[0065] Correlation analysis is a statistical method used to evaluate whether there is a statistical association between two or more variables, as well as the strength and direction of this association. In S21, correlation analysis is used to identify which physiological data have a significant impact on the degree of pain, so as to better understand the uniqueness of each physiological data.

[0066] In the present invention, physiological data are used as influencing factors for establishing a mathematical model, and the Pearson correlation coefficient r is calculated for each influencing factor x and pain level y:

[0067] Among them, x i is the physiological data value of the i-th sample, y i is with x i The corresponding pain level of the i-th sample, is the mean of the physiological data, is the mean pain level, n is the sample size;

[0068] When the Pearson correlation coefficient r = 1, it means that the influencing factor x and the pain level y are completely positively correlated, that is, the increase of one variable is always accompanied by the increase of the other variable; when r = -1, it means that the influencing factor x and the pain level y are completely negatively correlated, that is, the increase of one variable is always accompanied by the decrease of the other variable; when r = 0, it means that there is no linear correlation between the influencing factor x and the pain level y; the closer the absolute value of r is to 1, the stronger the linear relationship between the influencing factor x and the pain level y, and the closer it is to 0, the weaker the linear relationship.

[0069] The correlation coefficient is combined with the entropy weight method to calculate the weight of the influencing factors. The entropy weight method is an objective weight allocation method that determines the weight based on the discreteness of the data rather than subjective judgment. The combination of the two ensures that the weight allocation not only considers the discreteness of the data, but also the actual correlation strength between the influencing factors and the degree of pain.

[0070] Calculate the entropy value e of each influencing factor j : in, f ij is the value of the i-th sample on the j-th influencing factor, p ij represents the weight of the i-th sample on the j-th influencing factor, and m is the total number of evaluation objects;

[0071] The coefficient of difference d reflects the degree of dispersion of the indicator. j :d j =1-e j , combined with the correlation adjustment, the coefficient of difference is d j ′=d j ·|r j|, where r j is the correlation coefficient between the jth influencing factor x and the pain level y;

[0072] Use the adjusted coefficient of variation to calculate the weight w of each influencing factor j :

[0073]

[0074] After calculating the weights, the corresponding mathematical model is obtained: p is the predicted pain level, β 0 is the intercept term.

[0075] In some specific embodiments, in step S3, the following sub-steps are included:

[0076] S31, encapsulate the mathematical model into an independent module, and define the interface between the mathematical model and the evaluation system, including the input data format, output data format, and calling method involved;

[0077] S32, integrating the packaged mathematical model module into the evaluation system, realizing the connection between the system and the data source, and ensuring that the system can obtain the physiological data to be evaluated in real time;

[0078] S33, inputting the patient's real-time physiological data in the early postoperative period into the evaluation system, using a mathematical model to obtain a real-time pain level value, and the evaluation system calculates and gives adjustment suggestions for the ice compress intensity based on the pain level value;

[0079] Ice intensity g p : Use piecewise function to output the numerical code according to the predicted pain level p, Among them, a, b, and c are the thresholds of different pain levels in the early postoperative period, and 1, 2, 3, and 4 are the output numerical codes of the ice compress intensity.

[0080] In some specific implementation schemes, in step S31, the mathematical model is encapsulated by writing a model encapsulation code in a programming language (such as Python, Java), including loading the model file, defining data preprocessing and post-processing functions, and realizing the model prediction function. The communication protocol (such as HTTP, WebSocket) and data format (such as JSON, XML) between the real-time evaluation system and the mathematical model module must be clarified. The input interface should accept the data format sent by the real-time evaluation system, and the output interface should return the data format of the evaluation result.

[0081] In some specific implementation schemes, in step S32, the evaluation system sends the preprocessed data to the mathematical model module through the defined interface, the evaluation system receives the evaluation results returned by the mathematical model module, and performs necessary post-processing (such as parsing JSON data, converting data formats); the evaluation system should have the ability to receive data sources in real time and perform comprehensive postoperative nursing management and evaluation of gynecological tumors.

[0082] In some embodiments, in step S4, the drug amount k p : Use piecewise function to output the numerical code according to the predicted pain level p, d, e, and f are the thresholds for different pain levels in the late postoperative period, and 1, 2, 3, and 4 are the numerical codes for the output drug amounts.

[0083] In some specific embodiments, the thresholds for different pain severity ranges are set based on clinical experience, experimental data, or expert experience.

[0084] For example, in the early stage after gynecological tumor surgery, the thresholds of different pain levels are a=3 (mild pain), b=6 (moderate pain), and c=9 (severe pain). The numerical code output by the evaluation system is 1 for "continue to apply ice for 1 hour", 2 for "continue to apply ice for 1-2 hours", 3 for "continue to apply ice for 2-3 hours", and 4 for "other suggestions or notify the doctor".

[0085] The evaluation system uses a piecewise function g(p) to output a numerical encoding based on the predicted pain level p: When the pain level predicted by the mathematical model is p=2 (mild pain), then g(2)=1, and the output value is coded as 1, indicating "continue to apply ice for 1 hour". When the predicted pain level is p=10 (extreme pain), then g(10)=4, and the output value is coded as 4, indicating "other suggestions or notify a doctor", and immediate medical intervention is required.

[0086] It should be noted that by distinguishing the changes in patients' pain perception in the early and late postoperative periods, and by accurately evaluating and predicting the pain level, we can make personalized recommendations for pain management plans based on the predicted pain level, fully considering the individual differences and pain characteristics of patients, achieving personalized treatment, and ensuring that ice therapy or drug adjustments are always matched with the patient's pain level to achieve the best analgesic effect, while avoiding adverse reactions caused by excessive cold stimulation. By combining accurate evaluation with personalized treatment, we can significantly reduce patients' pain and discomfort, thereby improving the overall quality of postoperative recovery.

[0087] The present invention provides an intelligent system for comprehensive nursing management and evaluation method after gynecological tumor surgery, comprising:

[0088] The data management module is used to input the patient's physiological data and store the patient's basic information for classification management;

[0089] Data processing module, used to pre-process the collected physiological data to ensure the consistency and comparability of the data;

[0090] a mathematical model for outputting predicted pain assessment data based on the physiological data;

[0091] The evaluation system is used to output adjustment suggestions for ice compress intensity and matching suggestions for drug dosage based on pain assessment data in the early and late stages after surgery respectively.

[0092] In some specific embodiments, the patient's physiological data is input into a data management module, and the patient's basic information is stored for classification management. The data management module inputs the collected physiological data into a data processing module to preprocess the collected physiological data. The data processing module inputs the preprocessed physiological data into a mathematical model to predict pain assessment data. The assessment system outputs adjustment recommendations for ice compress intensity in the early postoperative period based on the pain assessment data, and outputs matching recommendations for drug dosage in the later postoperative period.

[0093] It should be noted that the intelligent system can automatically complete the collection, processing and analysis of patients' physiological data, greatly reducing the workload of medical staff. Medical staff only need to focus on the evaluation results and the formulation and implementation of treatment plans, without spending a lot of time on tedious data processing. Based on a large number of patients' historical data and real-time monitoring results, the system uses data analysis and mathematical algorithms to provide scientific decision-making support for medical staff, including pain management strategies, ice compress intensity adjustment recommendations, and drug treatment plans, which help medical staff make more reasonable and effective nursing decisions to ensure that patients get the best treatment results.

[0094] The above is based on the ideal embodiment of the present invention. Through the above description, it is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the attached claims rather than the above description, and it is intended to include all changes within the meaning and scope of the equivalent elements of the claims. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0095] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A comprehensive nursing management and evaluation method for gynecological tumor surgery, characterized in that: The following steps are involved: S1. Collect physiological data and subjective data of different patients at different periods after surgery, and pre-process the collected physiological data; S2. Build a mathematical model to assess pain severity based on physiological and subjective data; S3, integrating the mathematical model into the evaluation system, receiving the patient's real-time physiological data in the early postoperative period, using the mathematical model to obtain the real-time pain level value, and according to the pain level value, the evaluation system calculates and gives the adjustment suggestion of the ice compress intensity; S4. The evaluation system receives the patient's real-time physiological data in the late postoperative period, and uses a mathematical model to obtain a real-time pain level value. Based on the pain level value, the evaluation system calculates and gives a matching recommendation for the amount of medication; In step S2, the establishment of the mathematical model includes the following steps: S21, performing correlation analysis on the physiological data screened in step S12; S22. Assign weights to physiological data that influence pain; S23, outputting a mathematical model for assessing pain severity; The physiological data are used as influencing factors for establishing a mathematical model, and the Pearson correlation coefficient r is calculated for each influencing factor x and pain level y: Among them, x i is the physiological data value of the i-th sample, y i is with x i The corresponding pain level of the i-th sample, is the mean of the physiological data, is the mean pain level, n is the sample size; Calculate the entropy value e of each influencing factor j : in, f ij is the value of the i-th sample on the j-th influencing factor, p ij Indicates the weight of the i-th sample on the j-th influencing factor; The coefficient of difference d reflects the degree of dispersion of the indicator. j :d j =1-e j , combined with the correlation adjustment, the coefficient of difference is d j ′=d j ·r j , where r j is the jth influencing factor x and the correlation coefficient of pain level y; Use the adjusted coefficient of variation to calculate the weight w of each influencing factor j : After calculating the weights, the corresponding mathematical model is obtained: p is the predicted pain level and β0 is the intercept term.

2. A method for comprehensive nursing management and evaluation after gynecological tumor surgery according to claim 1, characterized in that: In the step S1, the following sub-steps are included: S11. Collect physiological data of different patients in the early and late stages after surgery. When collecting physiological data of patients, use a pain rating scale to collect subjective data of pain. S12. From the collected physiological data, select features that are highly correlated with pain changes in subjective data; S13. Clean, standardize and normalize the collected physiological data to ensure the consistency and comparability of the data.

3. A method for comprehensive nursing management and evaluation after gynecological tumor surgery according to claim 2, characterized in that: In step S11, the initial stage is 1-2 days after surgery, and the late stage is more than 2 days after surgery; the physiological data include: skin conductivity, muscle potential changes, heart rate variability, blood pressure, respiratory rate and skin temperature; the pain rating scale is one of VAS or NRS.

4. A method for comprehensive nursing management and evaluation after gynecological tumor surgery according to claim 1, characterized in that: In the step S3, the following sub-steps are included: S31, encapsulate the mathematical model into an independent module, and define the interface between the mathematical model and the evaluation system, including the input data format, output data format, and calling method involved; S32, integrating the packaged mathematical model module into the evaluation system, realizing the connection between the system and the data source, and ensuring that the system can obtain the physiological data to be evaluated in real time; S33, inputting the patient's real-time physiological data in the early postoperative period into the evaluation system, using a mathematical model to obtain a real-time pain level value, and the evaluation system calculates and gives adjustment suggestions for the ice compress intensity based on the pain level value; Ice intensity g p : Use piecewise function to output the numerical code according to the predicted pain level p, Among them, a, b, and c are the thresholds of different pain levels in the early postoperative period, and 1, 2, 3, and 4 are the output numerical codes of the ice compress intensity.

5. A method for comprehensive nursing management and evaluation after gynecological tumor surgery according to claim 4, characterized in that: In step S4, the drug amount k p : Use piecewise function to output the numerical code according to the predicted pain level p, d, e, and f are the thresholds for different pain levels in the late postoperative period, and 1, 2, 3, and 4 are the numerical codes for the output drug amounts.

6. A method for comprehensive nursing management and evaluation after gynecological tumor surgery according to claim 5, characterized in that: The thresholds for different pain severity ranges are set based on clinical experience, experimental data or expert experience.

7. An intelligent system for comprehensive nursing management and evaluation method for gynecological tumor surgery based on any one of claims 1-6, characterized in that: include: The data management module is used to input the patient's physiological data and store the patient's basic information for classification management; Data processing module, used to pre-process the collected physiological data to ensure the consistency and comparability of the data; a mathematical model for outputting predicted pain assessment data based on the physiological data; The evaluation system is used to output adjustment suggestions for ice compress intensity and matching suggestions for drug dosage based on pain assessment data in the early and late stages after surgery respectively.

8. The intelligent system for comprehensive nursing management and evaluation method for gynecological tumor surgery according to claim 7 is characterized by: The patient's physiological data is input into the data management module, and the patient's basic information is stored for classification management. The data management module inputs the collected physiological data into the data processing module to preprocess the collected physiological data. The data processing module inputs the preprocessed physiological data into the mathematical model to predict pain assessment data. The assessment system outputs adjustment suggestions for ice compress intensity in the early postoperative period based on the pain assessment data, and outputs matching suggestions for drug dosage in the later postoperative period.

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